Reconstruction of Genetic Regulatory Networks Based on the Posterior Probabilities of Gene Regulations

Recent advances in high throughput microarray data have enabled the learning of the structure and operation of gene regulatory networks. This paper proposes a novel approach for reconstruction of gene regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and exploiting efficient computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, and thereby it reduces significantly the inference errors. Simulation results corroborate the superior performance of the proposed approach relative to the existing state-of-the-art algorithms.